Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells

Daniel Ramsköld, Shujun Luo, Yu Chieh Wang, Robin Li, Qiaolin Deng, Omid R. Faridani, Gregory A. Daniels, Irina Khrebtukova, Jeanne F. Loring, Louise C. Laurent, Gary P. Schroth, Rickard Sandberg

Research output: Contribution to journalArticlepeer-review

1075 Scopus citations


Genome-wide transcriptome analyses are routinely used to monitor tissue-, disease-and cell type-specific gene expression, but it has been technically challenging to generate expression profiles from single cells. Here we describe a robust mRNA-Seq protocol (Smart-Seq) that is applicable down to single cell levels. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which enhances detailed analyses of alternative transcript isoforms and identification of single-nucleotide polymorphisms. We determined the sensitivity and quantitative accuracy of Smart-Seq for single-cell transcriptomics by evaluating it on total RNA dilution series. We found that although gene expression estimates from single cells have increased noise, hundreds of differentially expressed genes could be identified using few cells per cell type. Applying Smart-Seq to circulating tumor cells from melanomas, we identified distinct gene expression patterns, including candidate biomarkers for melanoma circulating tumor cells. Our protocol will be useful for addressing fundamental biological problems requiring genome-wide transcriptome profiling in rare cells.

Original languageEnglish
Pages (from-to)777-782
Number of pages6
JournalNature Biotechnology
Issue number8
StatePublished - Aug 2012


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